| Full text | |
| Author(s): Show less - |
Silva, Talita M.
[1, 2]
;
Borniger, Jeremy C.
[3]
;
Alves, Michele Joana
[2]
;
Correa, Diego Alzate
[2]
;
Zhao, Jing
[4]
;
Fadda, Paolo
[5]
;
Toland, Amanda Ewart
[5, 6]
;
Takakura, Ana C.
[7]
;
Moreira, Thiago S.
[1]
;
Czeisler, Catherine M.
[2]
;
Otero, Jose Javier
[2]
Total Authors: 11
|
| Affiliation: | [1] Univ Sao Paulo, Inst Biomed Sci, Dept Physiol & Biophys, Sao Paulo - Brazil
[2] Ohio State Univ, Coll Med, Dept Pathol, Div Neuropathol, Columbus, OH 43210 - USA
[3] Cold Spring Harbor Lab, POB 100, Cold Spring Harbor, NY 11724 - USA
[4] Ohio State Univ, Coll Dent, Dept Biomed Informat, Columbus, OH 43210 - USA
[5] Ohio State Univ, Genom Shared Resource Comprehens Canc Ctr, Columbus, OH 43210 - USA
[6] Ohio State Univ, Coll Med, Dept Canc Biol & Genet, Columbus, OH 43210 - USA
[7] Univ Sao Paulo, Inst Biomed Sci, Dept Pharmacol, Sao Paulo - Brazil
Total Affiliations: 7
|
| Document type: | Journal article |
| Source: | Journal of Neurophysiology; v. 125, n. 4, p. 1164-1179, APR 2021. |
| Web of Science Citations: | 0 |
| Abstract | |
Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availability of open-source programming languages, and the objectivity permitted in their data analysis. We used classification-based algorithms, including random forest, gradient boosted machines, support vector machines, and neural networks, to test the hypothesis that the animal genotypes could be separated into their genotype based on interpretation of neurophysiological recordings. We then interrogate the models to identify what were the major features utilized by the algorithms to designate genotype classification. By using raw EEG and respiratory plethysmography data, we were able to predict which recordings came from genotype class with accuracies that were significantly improved relative to the no information rate, although EEG analyses showed more overlap between groups than respiratory plethysmography. In comparison, conventional methods where single features between animal classes were analyzed, differences between the genotypes tested using baseline neurophysiology measurements showed no statistical difference. However, ML/AI workflows successfully were capable of providing successful classification, indicating that interactions between features were different in these genotypes. ML/AI workflows provide new methodologies to interrogate neurophysiology data. However, their implementation must be done with care so as to provide high rigor and reproducibility between laboratories. We provide a series of recommendations on how to report the utilization of ML/AI workflows for the neurophysiology community. NEW \& NOTEWORTHY ML/AI classification workflows are capable of providing insight into differences between genotypes for neurophysiology research. Analytical techniques utilized in the neurophysiology community can be augmented by implementing ML/AI workflows. Random forest is a robust classification algorithm for respiratory plethysmography data. Utilization of ML/AI workflows in neurophysiology research requires heightened transparency and improved community research standards. (AU) | |
| FAPESP's process: | 15/23376-1 - Retrotrapezoid nucleus, respiratory chemosensitivity and breathing automaticity |
| Grantee: | Thiago dos Santos Moreira |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/03994-0 - Ultrastructural analyses of axon pathology in PHOX2b-astrocyte ablated mice |
| Grantee: | Talita de Melo e Silva |
| Support Opportunities: | Scholarships abroad - Research Internship - Post-doctor |
| FAPESP's process: | 19/01236-4 - Effects of pharmacological and non-pharmacological treatments on respiratory changes observed in a murine model of Parkinson's disease |
| Grantee: | Ana Carolina Takakura Moreira |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 17/12678-2 - Participação dos astrócitos localizados na superfície ventrolateral do bulbo nas respostas ventilatórias à hipercapnia e hipóxia |
| Grantee: | Talita de Melo e Silva |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |